Advertising within social networks

ABSTRACT

An online social network is provided. A sentiment is determined for each of a plurality of users of an online social network (OSN) in relation to a first product. A category is determined for each of the plurality of users based, at least in part, on the sentiment of each of the plurality of users, respectively. A group including a first user and a second user of the plurality of users is generated based, at least in part, on the category of each of the first user and the second user and a relationship within the OSN between the first user and the second user. An advertisement is presented to the first user. An indication is presented to the first user that the advertisement is also presented to the second user.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of social media,and more particularly to advertising within a social network.

An online social network (OSN) is a platform that allows users to buildrelationships with other users of the OSN who share interests,activities, backgrounds, or real-life connections. Certain socialnetworks allow a user to create a user profile, by which the userprovides individual content via the OSN. Users share content with otherusers of the OSN including but not limited to, text, audio, video, orimage content. Additionally, an OSN allows a user to control the amountof personal content of the user that is accessible by other users of theOSN. Furthermore, users have the ability to suggest certain content toother users within the OSN. Some OSNs are internet-based servicesaccessible to users who are physically located in different, andpotentially distant, geographic locations.

Online advertising is a form of advertising that uses the Internet todeliver promotional marketing messages to consumers. Some common formsof online advertising include email marketing, search engine marketing,social media marketing, display advertising, web banner advertising, andmobile advertising. Online advertising typically involves both apublisher, who integrates advertisements into its online content, and anadvertiser, who provides the advertisements to be displayed on thepublisher's content. Online advertising is widely used across manyindustries.

Sentiment analysis refers to the use of natural language processing,text analysis, and computational linguistics to identify and extractsubjective information in source materials. Sentiment analysis aims todetermine the attitude or opinion of a speaker or writer in relation tosome event, topic or overall contextual polarity of a document. Theattitude is an affective state, an evaluation, a judgment, an intendedemotional communication, an opinion, or any other identifier.

SUMMARY

According to one embodiment of the present disclosure, a method foradvertising within a social network is provided. The method includesdetermining, by one or more computer processors, a sentiment of each ofa plurality of users of an online social network (OSN) in relation to afirst product; determining, by one or more computer processors, acategory of each of the plurality of users based, at least in part, onthe sentiment of each of the plurality of users, respectively;generating, by one or more computer processors, a group including afirst user and a second user of the plurality of users based, at leastin part, on the category of each of the first user and the second userand a relationship within the online social network (OSN) between thefirst user and the second user; presenting, by one or more computerprocessors, an advertisement to the first user; and presenting, by oneor more processors, an indication to the first user that theadvertisement is also presented to the second user.

According to another embodiment of the present disclosure, a computerprogram product for advertising within a social network is provided. Thecomputer program product comprises a computer readable storage mediumand program instructions stored on the computer readable storage medium.The program instructions include program instructions to determine asentiment of each of a plurality of users of an online social network(OSN) in relation to a first product; program instructions to determinea category of each of the plurality of users based, at least in part, onthe sentiment of each of the plurality of users, respectively; programinstructions to generate a group including a first user and a seconduser of the plurality of users based, at least in part, on the categoryof each of the first user and the second user and a relationship withinthe online social network (OSN) between the first user and the seconduser; program instructions to present an advertisement to the firstuser; and program instructions to present an indication to the firstuser that the advertisement is also presented to the second user.

According to another embodiment of the present disclosure, a computersystem for advertising within a social network is provided. The computersystem includes one or more computer processors, one or more computerreadable storage media, and program instructions stored on the computerreadable storage media for execution by at least one of the one or moreprocessors. The program instructions include program instructions todetermine a sentiment of each of a plurality of users of an onlinesocial network (OSN) in relation to a first product; programinstructions to determine a category of each of the plurality of usersbased, at least in part, on the sentiment of each of the plurality ofusers, respectively; program instructions to generate a group includinga first user and a second user of the plurality of users based, at leastin part, on the category of each of the first user and the second userand a relationship within the online social network (OSN) between thefirst user and the second user; program instructions to present anadvertisement to the first user; and program instructions to present anindication to the first user that the advertisement is also presented tothe second user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a computingenvironment, in accordance with an embodiment of the present disclosure;

FIG. 2 is a flowchart depicting operations for an advertising program,on a computing device within the computing environment of FIG. 1, inaccordance with an embodiment of the present disclosure;

FIG. 3 is a flowchart depicting additional operations of the advertisingprogram, on a computing device within the computing environment of FIG.1, in accordance with an embodiment of the present disclosure; and

FIG. 4 is a block diagram of components of a computing device executingoperations of an advertising program, in accordance with an embodimentof the present disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure recognize that social networkutilities are an integral portion of an individual's daily interactions,such as during personal or professional activities. Often times,businesses look to social media in order to promote their products orservices in order to increase their clientele. Increased online exposurecan lead to improved sales of a service or product. Embodiments of thepresent disclosure recognize that many OSNs encourage advertising.Embodiments of the present disclosure provide improved advertising bygrouping users of a social network based on a plurality of factors that,alone or in combination, suggest the extent to which each user is morelikely to recommend or detract from a product or service. For example,the plurality of factors includes expressed sentiments of each OSN userin relation to a product or service. In another example, users having aconsistent history of purchasing a product or service, or even productsor services under the same brand or seller are determined to be morelikely to recommend rather than detract. In yet another example, userswho bought a product, but returned it quickly (i.e., within a specifiedtime period), are determined more likely to detract than to recommend.Embodiments of the present disclosure provide drawing inferences basedon user behavior (e.g., the user behavior mentioned in one or more ofthe preceding examples). For example, a person has owned a vehicle forthree and a half years and has a pattern of buying a new vehicle everyfour years. Based on this user behavior, an inference is drawn that theuser is considering the purchase of a new vehicle.

Embodiments provide utilizing a score of the likelihood ofrecommendation or detraction of a user for a product. This score is apromotion/detraction likelihood score for a product. For example, NetPromoter Score (NPS) is a known methodology for measuring customerloyalty. The main idea of the methodology is that customer loyalty canbest be predicted by the answer to a single question: “How likely areyou to recommend (company name/product) to a friend or colleague?”.Respondents answer on a scale of zero (not at all likely) to ten(extremely likely). Based on the response to this question, respondentsare categorized as Promoters (9-10), Passives (7-8) or Detractors (0-6).A Net Promoter Score of a product or company is the percentage ofPromoters minus the percentage of Detractors.

Many companies believe and invest in improving their Net Promoter Scoreas a means to becoming more successful and customer-focused. This beliefis based on the assumption that the more individuals who say they are“likely to recommend a company/product to a friend or colleague”, themore successful the product is likely to be. Embodiments recognize thatan impartial personal recommendation improves success for a product.Further, a good Net Promoter Score increases the likelihood of impartialrecommendations, so there is incentive to improving the Net PromoterScore of a company or product. Alternatives to the Net Promoter Scoreinclude the Net Endorser score of the Company Health Check Index of RedShift Research and other measures of likelihood of recommending ordetracting from a product. Additionally, in some embodiments, a classicNPS (or equivalent) is not available and an estimated score of thepromotion/detraction likelihood score (estimated from other metricsabout the relationship of the consumer with the company and/or product)is used. The term “Net Promoter”, “Net Promoter Score”, “NPS”, “NetEndorser”, “Net Endorser” and the like may be subject to trademarkrights in various jurisdictions throughout the world and are used hereonly in reference to the products or services properly denominated bythe marks to the extent that such trademark rights may exist.

In one embodiment, the propensity to recommend, detract, or purchase isprovided for by a separate system or server. For example, embodimentsprovide looking up a Net Promoter Score, or another form of existingendorsement score, for a person regarding a product. Alternatively, ifno existing score is available, some embodiments provide estimating ascore of a likelihood of a person to promote or detract from a product.Thus, a user of an OSN is determined to be a recommender, a passive, ora detractor for an advertised product or service based on apromotion/detraction likelihood score of the user regarding the product.Further, whether a user is a prospect is determined based on whether theuser is a potential purchaser of an advertised product or service. Arecommender is a user who is determined to be more likely to recommendthan detract from a product or service. A passive is a user who is notdetermined to be a recommender or a detractor. A detractor is a user whois determined to be more likely to detract from than recommend a productor service. A prospect is a user whose purchase decision is determinedto be positively or negatively influenced by receiving a recommendationor detraction, respectively. In various embodiments, a prospect is auser who is either in the process of making a purchase decision or whois otherwise considering purchasing a product or service. For example, aprospect considering a purchase decision for a product is influencedpositively by the prospect receiving a recommendation for the product,and the prospect purchases the product. Conversely, a prospectconsidering a purchase receives a detracting opinion of a detractor andis dissuaded from making the purchase. In one embodiment, it isdetermined whether a user is a prospect and also whether the user is arecommender, a passive, or a detractor. For example, a first user and asecond user are each determined to be one of a recommender, a passive,or a detractor. In this case, the first user is determined to be aprospect and the second user is determined not to be a prospect. In oneembodiment, a user who is a recommender with respect to a first productis a detractor with respect to a second product.

Embodiments provide for generating one or more groups, each of whichincludes one or more users of the OSN. Each group member is arecommender or a detractor and each group member is, in some cases, alsobe a prospect. A group includes any combination of recommenders,prospects, or detractors. In various examples, a group includes at leastone of a recommender, a prospect, or a detractor. In one embodiment, agroup includes one or more recommenders and one or more prospects and,in another embodiment, the group also includes one or more detractors.In one example, a group including a recommender, a prospect, and adetractor is assembled for the purpose of sparking debate among thegroup members. For example, the debate is between two models of aproduct. Alternatively, the debate is between two competing brands orproducts offered by a single advertiser. In some embodiments, a groupincludes at least one group member who serves as a conversation catalystin that the group member has a high likelihood of promoting theinitiation of or continuation of conversation. Such a catalyst groupmember is identified based on a previous pattern of conversationalactivity of the group member. For example, a user with highconversational activity is added to a group as a catalyst group member,wherein the conversational activity is, in various examples, activitywithin other groups or activity in other areas of the OSN.

Some embodiments provide that group members are related or associatedwith one another. A relation between two users of an OSN is, in oneembodiment, a relationship within social network between the twomembers. In various embodiments, a relationship is express or implied.An expressed relationship is a connection initiated by one of themembers (e.g., a friend or connection request). An implied relationshipis a connection determined to exist between two users based, forexample, on a prior history of interaction between the users oridentified commonalities between the users.

An advertisement is presented to the members of a group. Theadvertisement is identified as an advertisement targeted to the group.In one embodiment, the advertisement is presented to each group memberwithin a certain time frame. In various examples, the duration of thetime frame is based on the content of the advertisement, the product orservice advertised, the company advertising the product or service, orother factors. Further, the duration of the time frame is, in variousexamples, pre-determined or algorithmically-determined.

Various embodiments provide for receiving a selection of anadvertisement from a user, receiving comments about the selectedadvertisement from the user, presenting comments to group members,presenting comments from group members to other group members (e.g., byrestricting access to comments about an advertisement to group members),or a combination thereof.

In one embodiment, viewing a group advertisement is an impression. Inthe context of advertising, an impression is a measure of the number oftimes an advertisement is presented to users. In one embodiment, animpression is measurement of responses to a request from a device of auser, which is filtered from robotic activity and error codes, and isrecorded at a point as close as possible to an opportunity to see thepage by the user. Under the cost per impression (CPI) model, the cost ofadvertising is determined based on the number of impressions of theadvertisement. In one embodiment, providing an advertisement to a groupmember counts as at least one impression. For example, an advertisementpresented to two group members is counted as two impressions. In oneembodiment, a user accessing a group discussion about an advertisementis counted as an impression. In one embodiment, an impression is weighedbased on the scores of the group members. For example, a group memberwho is a prospect views a discussion about an advertisement in whichother group members are participating. In this case, the number ofimpression for which the prospect viewing the discussion counts is basedon (i) the average recommender-detractor score of the other groupmembers being above a pre-determined threshold, (ii) the average scoreof the group members who have participated in the discussion being abovea pre-determined threshold, (iii) the number of recommenders in thegroup, (iv) the proportion of recommenders to detractors, (v) whetherthe prospect participates in the discussion and, if so, the content ofany comments provided by the prospect, or any combination thereof. Inone embodiment, the number of impressions for each viewing (or otheraccess) by a user to an advertisement or discussion is reduced forviewings (or accesses) subsequent to the first. In one embodiment, theselection of an advertisement for presentation to a user is based on thedegree to which the user has been exposed to the product previously. Forexample, a user who has viewed, for a first product, a firstadvertisement, a second advertisement, and a discussion regarding thefirst advertisement is determined to have a high degree of exposure tothe first product, based on which an advertisement for a second productis selected for presentation to the user.

The implementation of the embodiments previously discussed can takemultiple forms and are exemplified in detail through the Figures below.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer-readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The present disclosure will now be discussed in detail with reference tothe figures. FIG. 1 is a functional block diagram illustrating computingenvironment 100, specifically a social networking environment, inaccordance with an embodiment of the present disclosure. Computingenvironment 100 includes computing device 102 and client device 130,both connected over network 120. Computing device 102 includes onlinesocial network (OSN) program 104, advertising program 106 andadvertisement data 108. Client device 130 includes client user interface(UI) 132.

In various embodiments of the present disclosure, each of computingdevice 102 and client device 130 may be a desktop computer, a laptopcomputer, a tablet computer, a specialized computer server, asmartphone, or any other computer system known in the art. In certainembodiments of the present invention, each of computing device 102 andclient device 130 is representative a computer system utilizingclustered computers and components that act as a single pool of seamlessresources when accessed through network 120, as is common in datacenters and with cloud computing applications. In general, each ofcomputing device 102 and client device 130 is representative of anyprogrammable electronic device or combination of programmable electronicdevices capable of executing advertising program 106, accessingadvertisement data 108, and accessing an OSN provided by OSN program104. In general, client device 130 can be any computing device or acombination of devices with access to, and capable of executingadvertising program 106 and that is capable of requesting, receiving andpassing information to and from OSN program 104. Each of client device130 and computing device 102 include internal and external hardwarecomponents, as depicted and described in FIG. 4.

In this exemplary embodiment, OSN system 104, advertising program 106and advertisement data 108 are stored on computing device 102. In otherembodiments, one or more of OSN program 104, advertising program 106 andadvertisement data 108 may reside on another computing device, providedthat each can access and is accessible by each other of OSN program 104,advertising program 106 and advertisement data 108. In this exemplaryembodiment, client UI 132 is stored on client device 130. In otherembodiments, client UI 132 may reside on another computer device,provided that it is accessible by OSN program 104, advertising program106 and advertisement data 108. In yet other embodiments, one or more ofOSN program 104, advertising program 106, advertisement data 108, andclient UI 132 may be stored externally and accessed through acommunication network, such as network 120. Network 120 can be, forexample, a local area network (LAN), a wide area network (WAN) such asthe Internet, or a combination of the two, and may include wired,wireless, fiber optic or any other connection known in the art. Ingeneral, network 120 can be any combination of connections and protocolsthat supports communications between computing device 102 and clientdevice 130, in accordance with a desired embodiment of the presentinvention.

OSN program 104 resides on computing device 102 and, in one embodiment,operates to provide an OSN. In some embodiments, OSN program 104facilitates the maintenance of social network contacts and thecommunication or sharing of information between such contacts. As usedherein, a connection is the link shared between two users of an OSN. Forexample, a connection exists between a user of the OSN and other usersof the OSN on a list of friends or contacts of the user, which indicatesthe other users of the OSN to whom the user is linked by a connection.As used herein, a contact is the person on either side of a connection.In various examples, a user is connected to another user who is anacquaintance, a co-worker, a friend, a parent, a child, a classmate, ora spouse. In other examples, the user is connected to another userthrough personal, business- or work-related interactions. In oneembodiment, computing device 102 is a server computer system accessibleto a plurality of users of OSN program 104, including a user of clientdevice 130. In one such embodiment, client UI 132 may be a userinterface by which a user (e.g., a user of client device 130) accessesan OSN provided by OSN program 104 via network 120. In variousembodiments, received communications and information are presented tothe user of computing device 102 by way of client UI 132 and messagesand instructions are received from the user by way of client UI 132.

In one embodiment, OSN program 104 of computing device 102 allows OSNusers to share messages and content with other users and with groups ofthe OSN. OSN program 104 coordinates the sending and receiving of sharedmessages and content among OSN users. In one embodiment, OSN program 104receives content, messages and information from individual users. OSNprogram 104 sends the received content, messages and information toother OSN users. In various embodiments, the content includes textcontent, media rich content, such as audio, pictures and video, or acombination thereof. The messages also include, for example, links towebsites or profiles of other users. In another example, such messagesinclude, at least in part, information concerning recent activityperformed by users of the OSN like updating a profile, joining a group,and recommending posts within the OSN.

In one embodiment, advertising program 106 operates to present anadvertisement to group members of an OSN. In this embodiment,advertising program 106 selects an advertisement (e.g., an advertisementof advertisement data 108). Advertisement selection considers thepotential effectiveness of an advertisement for a group. As an example,advertisement data 108 includes two advertisements: an advertisement forvehicle A and an advertisement for vehicle B. In this example, a groupfor which advertisement program 106 selects an advertisement includes agreater number of vehicle A promoters than vehicle B promoters, soadvertisement program 106 presents the advertisement for vehicle A tothe assembled group. In one embodiment, advertising program 106 selectsan advertisement that is most likely to result in a positive influenceto a purchase decision by a prospect based on the sentiment of each userof the group. For example, advertising program 106 assembles a groupthat includes a prospect, one or more recommenders who have a highdegree of influence on the prospect, and no detractors (or,alternatively, only detractors with low degrees of influence on theprospect). In this example, advertising program 106 selects anadvertisement from among advertisements that promote one or both of aproduct for which the user is making the purchase decision or a productrelated thereto (e.g., a competing product). Further, advertisingprogram 106 selects an advertisement for which the assembled group hasthe highest degree of influence over the purchase decision of theprospect.

In one embodiment, advertising program 106 operates to determine acategory for a user of the OSN (e.g., recommender, prospect, ordetractor) based, for example, on recommender-detractor score of theuser in relation to a product or service. In various examples, the scoreis based on a probability of a user to express a positive or negativeopinion regarding a product or service, a probability of a user topurchase or express a level of satisfaction with a certain product orservice, or other attitudes or tendencies of a user in relation to aproduct or service, such as those explained above.

In one embodiment, advertising program 106 operates to group users of anOSN (e.g. based on relationships of the users, a category or score ofeach user, or a combination thereof). The group includes one or moremembers from at least one category. In other embodiments, advertisementprogram 106 selects recommenders and prospects.

In one embodiment, advertising program 106 operates to present anadvertisement to the group members. In one embodiment, advertisingprogram 106 presents the advertisement to each group member at varioustimes within a given time frame. In some embodiments, the time frame isspecified in order to make it more likely for the group members to feelthat the other group members have seen the advertisement once thediscussion begins. In such an embodiment, the specific time frameincreases the probability of a more robust discussion including morecontributions from individual group members. In various examples, thetime frame is measured in whole or fractional minutes, hours, days,weeks, or other time periods. For example, a department store has aforty-eight hour sale on jeans over a holiday weekend. The advertisementbegins to run seven days prior to the start of the sale. Theadvertisement stops running at the end of the forty-eight hour period.In this example, one group member signs in to the OSN and is presentedwith the advertisement immediately on the first day the advertisement isrunning, whereas another group member does not log on and is notpresented with the advertisement until the fifth day that theadvertisement is running. In another example, the first 200 customers topurchase the newest smartphone on Cyber Monday receive a fifty percentdiscount on the item. The advertisement runs from Black Friday until theend of Cyber Monday. All group members are presented with theadvertisement during the few days the advertisement runs given they logon to the OSN. As another example, a jewelry company has a continuous“buy one necklace, get the second necklace free” sale. No timeconstraint is placed on the advertisement running, so one group memberis presented with the advertisement on the first day it begins runningand another group member is presented with the advertisement twelve dayslater. In one embodiment, advertising program 106 further operates toreceive comments regarding the advertisement. In one embodiment,advertising program 106 presents comments received from individual groupmembers to one or more other members of the group. In anotherembodiment, advertising program 106 functions to receive theadvertisement from advertisement data 108.

Advertisement data 108 is a database of information relevant to thedistribution of advertisements. In one embodiment, advertisement data108 includes information for each user of an OSN (e.g., an OSNmaintained by OSN program 104). In this embodiment, advertisement data108 includes information that advertising program 106 uses duringclassification and group assembly, including past activity of one ormore users. For example, the past activity of a user includes onlineactivity in relation to products contained in advertisements a user hasviewed, shared or recommended to other users. The past activity of auser includes information such as, for example, comments, contentprovided, whether that content was private or shared with other OSNusers, the content of others users that the user viewed, the amount oftime spent on the OSN by the user, and past activity of all the contactsof that user. In one embodiment, advertisement data 108 includesmetadata for each of one or more advertisements identifying a product orservice corresponding to the advertisement. In another embodiment, themetadata for an advertisement identifies a time frame during which theadvertisement is to be presented. In another embodiment, advertisementdata 108 includes multiple versions of each advertisement specific toindividual group members. For example, a version of an advertisement fora recommender identifies a referral code and offers a referral bonus tothe recommender if another group member (e.g., a prospect) purchases theadvertised product using the referral code. In this example, a versionof an advertisement for a prospect does not include the referral codeand referral bonus offer. Conversely, for example, the advertisement fora prospect includes a different offer, such as a ten percent discountfor new customers. In one embodiment, an advertisement for a detractorincludes a falsely-competing product for the purpose of sparking adebate initiated by the detractor. For example, Model A and Model B areboth long distance running shoes sold by the same company, but vary indesign. In this example, both models are advertised to members of agroup including a group member who is a detractor in relation to Model Ato increase the probability of the detractor initiating a discussionconcerning the two models.

FIG. 2 is a flowchart depicting the operational process of advertisingprogram 106 on computing device within the computing environment 100, inaccordance with an embodiment of the present disclosure.

In process 204, advertising program 106 determines a category of eachuser of the OSN. In one embodiment, advertising program 106 determinesthe category of a user based on a sentiment of the user. For example,advertising program 106 determines a sentiment of a user based on theuser data (e.g., profile content) of the user. In various examples, thesentiment of a user indicates a likelihood of the user to express arecommendation, express a detracting opinion, or make a purchasedecision. In this embodiment, advertising program 106 determines acategory for a user by comparing the sentiment of the user to one ormore pre-determined thresholds of each category. For example, asentiment for a user indicates an eighty-five percent likelihood thatthe user recommends a product, a ten percent likelihood that the usermakes a purchase decision regarding the product, and a seven percentlikelihood that the user expresses a detracting opinion regarding theproduct. Further, the pre-determined thresholds include: for therecommender category, an eighty percent likelihood to recommend theproduct; for the prospect category, a seventy-five percent likelihood tomake a purchase decision; and for the detractor category, a sixtypercent likelihood to express a detracting opinion. In this case,advertising program 106 compares the sentiment of the user to thethresholds of each category and determines that the category of the useris recommender. In another embodiment, a user does not exceed thethreshold of any categories, in which case advertising program 106 doesnot determine the user to belong to any of the categories. In oneembodiment, advertising program 106 determines a sentiment of a user.For example, advertising program 106 determines the sentiment based oninformation shared by a user in the OSN, including past activity of theuser. In one example, based on a sentiment of a first user thatindicates a high probability of the first user to recommend a product,advertising program 106 classifies the first user as a recommender. Inanother example, based on a sentiment of a second user that indicates ahigh probability of a second user to provide negative comments about aproduct, advertising program 106 classifies the second user as adetractor. In another example, based on a sentiment of a third userindicating a likelihood of the third user to make a purchase decisionregarding the product, advertising program 106 classifies the third useras a prospect. In one embodiment, advertising program 106 determines acategory for a user based, at least in part, on a relationship betweenthe user and one or more other users in the OSN. In this case, therelationship includes a frequency of interactions between the user andthe one or more other users, the number of times the user visits theprofile of each of the one or more other users, and the number of likes,photos, and links concerning a product or service of each of the userand the one or more other users.

In some embodiments, the sentiment of a user includes a degree ofinfluence of a user, which is a measure of how influential an opinion ofthe user is on the purchase decisions of a prospect. A user with a highdegree of influence is more likely to sway a purchase decision of aprospect relative to a user with a low degree of influence. In oneembodiment, advertising program 106 compares the degree of influence ofa member to one or more predetermined thresholds when selecting usersfor a group. For example, a group includes a recommender having a highdegree of influence (e.g., a degree of influence above a predeterminedthreshold) and a detractor having a low degree of influence (e.g., adegree of influence below a predetermined threshold). In one embodiment,advertising program 106 balances the recommenders and detractors withinin a group such that, collectively, the recommenders have a higherdegree of the influence than the detractors. Advertising program 106determines a degree of influence of a user based on the purchasedecisions made by other users after being exposed to an opinion (e.g.,viewing a comment or message containing the opinion) of the user. In oneembodiment, advertising program 106 determining the degree of influenceof a user includes utilizing statistical methods to determine thestatistical significance of the effect of the opinion of the user,thereby reducing the effect of confounding factors or other extraneousvariables. In another embodiment, the degree of influence of a user isbased, at least in part, on how likely the user is to provide anopinion. In another embodiment, the degree of influence of a user isparticular to one or more other users. For example, advertising program106 determines that a first user has a high degree of influence forpurchase decisions of a second user, but a low degree of influence forpurchase decisions by a third user. In this case, advertising program106 creates a group including the first user, the second user, and afourth user that has a low degree of influence for purchase decisions ofthe second user.

In process 206, advertising program 106 generates a group of usersbased, at least in part, on a category of each user. In one embodiment,the group includes a plurality of OSN users who share common connectionsor already have established relationships with each other and each fallinto at least one of the recommender category, the prospect category,and the detractor category in relation to a certain product or service.Advertising program 106 selects group members based, at least in part,on the classification occurring in process 204. For example, advertisingprogram 106 selects an assortment of recommenders and prospects. Theselection is in accordance with a configured group size, which includes,for example, a minimum and maximum number of group members of eachcategory. In one embodiment, each of the minimum number and the maximumnumber are operator-configured. The minimum and maximum numberscontribute to a specified ratio of each of the recommenders and each ofthe prospects that are required in each group. In another embodiment,advertising program 106 selects a number of recommenders and prospectsis based, at least in part, on the relationships between the groupmembers in the OSN. For example, advertising program 106 selects a largenumber of group members in order to obfuscate the selection of groupmembers to other users in the group. In yet another embodiment,advertising program 106 biases restrictions on group size toward groupssmall enough so that group members recognize one another, therebyincreasing the chance of a group members contributing to groupdiscussion. In one embodiment, advertising program 106 generates a groupbased, at least in part, on the connections, relationships, andfrequency of interactions among users. In another embodiment, theconfigured group size includes a proportion of recommenders toprospects. For example, a group has at least ten recommenders to everytwo prospects. In another example, a configuration specifies a group ofat least four prospects when there are twelve recommenders in a group.In one example, advertising program 106 receives user input specifying aconfigured group size from a user (e.g., a user of client device 130,via client UI 132). In other examples, advertising program 106 receivesthe configuration of the group size by retrieving the configuration froma database, such as advertisement data 108, or by algorithmicallydetermining the configuration. In some embodiments, advertisementprogram 104 determines generates a group based, at least in part, on agroup member recommending or purchasing a product.

In process 208, advertising program 106 labels an advertisement. In oneembodiment, the label identifies to a group member one or more of theother group members who receive the advertisement. In one embodiment,the advertisement presents the names of the other group members to whomthe advertisement is presented. For example, the group member receivingthe advertisement is presented with the names of the one or more othergroup members who have also received the advertisement (or a version ofthe advertisement). For example, the group member receiving theadvertisement is presented with the names of the one or more other groupmembers who also receive the advertisement (or a version of theadvertisement). In one embodiment, the recipients have a sharedconnection with one another, which increases the probability of arecommender within the group recommending the advertised products toother group members. In some embodiments, advertising program 106presents the advertisements in the form of a group advertisementcontainer, which contains separate but similar advertisements to bepresented to each group member. For example, two advertisements arecomplimentary. For example, a group advertisement container includes anadvertisement for surfboards and the same group advertisement containerincludes an advertisement for wetsuits. A discussion concerning bothproducts is more likely to generate positive recommendations ordetractions, respectively, due to the similarity of the productsadvertised.

In process 210, advertising program 106 presents each group member withthe labeled advertisement. Advertising program 106 presents theadvertisement in a time frame corresponding to the advertisement (e.g.,the time frame stored in advertisement data 108 in the metadata of theadvertisement). For example, the timeframe is dependent on theadvertisement topic. For example, the OSN (or, alternatively,advertising program 106) presents an advertisement to each group memberwithin the same day. Alternatively, the OSN (or, alternatively,advertising program 106) presents an advertisement to each group memberdays apart from each other. In one embodiment, advertising program 106receives a user selection of an advertisement. In some embodiments,advertising program 106 receives a comment from a group member inrelation to an advertisement and, in response, presents theadvertisement to other group members. In certain embodiments,advertising program 106 adjusts the time at which the OSN (or,alternatively, advertising program 106) presents the advertisement togroup members who have not yet seen the advertisement. For example,advertisement program 106 presents the advertisement to prospects withintwenty-four hours rather than forty-eight hours in response to the firstgroup member viewing the advertisement, wherein the original time frameis forty-eight hours. In certain embodiments, the OSN (or,alternatively, advertising program 106) presents two competingadvertisements to a group member. At least one of those advertisementsis a group advertisement described herein. In certain embodiments, theOSN (or, alternatively, advertising program 106) accounts for whethergroup discussion has already begun regarding a certain advertisementwhen determining the time at which the group member is presented withthe advertisement. In some embodiments, the extent an advertisementgenerated discussion in other groups is also considered when determiningthe time at which an advertisement is presented.

FIG. 3 is a flowchart depicting operations for executing additionalsteps of advertising program 106, on a computing device within thecomputing environment of FIG. 1, in accordance with an embodiment of thepresent disclosure. As an example, FIG. 3 is a flowchart depictingoperations 300 of advertising program 106, on computing device 102within computing environment 100.

In process 302, advertising program 106 further operates to providecomment space for a group member. For example, the group member commentson a particular advertisement via a comment box presented with theadvertisement. For example, advertising program 106 or OSN program 104provides the comment box in conjunction with a given advertisement. Inother embodiments, the comment box is provided (by, e.g., the OSN oradvertising program 106) alongside the advertisement. In otherembodiments, the comment box is provided (by, e.g., the OSN oradvertising program 106) separately from the advertisement. The commentbox contributes to the likelihood of a group member recommending orpurchasing a product. The comment box prompts a group member to providefeedback concerning the product displayed in the advertisement.

In process 304, advertising program 106 receives comments made byindividual group members. In one embodiment, advertisement program 106receives the comments as user input (e.g., from a user of user device130 via client UI 132) via a comment space provided by advertisingprogram 106. In other embodiments, advertising program 106 determinesthat a group member has provided a comment and, in response, advertisingprogram 106 receives the comment. In some embodiments, advertisingprogram 106 stores the comment in advertisement data 108.

In process 306, advertising program 106 presents the comments ofindividual group members to each of the other group members. Advertisingprogram 106 restricts the viewing capacity of the comments to groupmembers only. In one embodiment, the restriction on viewing abilityincreases the likelihood that group members contribute to groupdiscussion on a certain advertisement topic.

FIG. 4 is a block diagram of components of the computing deviceexecuting operations for targeting advertisements within a socialnetwork, in accordance with an embodiment of the present disclosure. Forexample, FIG. 4 is a block diagram of computing device 102 withincomputing environment 100 executing operations of advertising program106.

It should be appreciated that FIG. 4 provides only an illustration ofone implementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

Computing device 102 includes communications fabric 402, which providescommunications between computer processor(s) 404, memory 406, persistentstorage 408, communications unit 410, and input/output (I/O)interface(s) 412. Communications fabric 402 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric402 can be implemented with one or more buses.

Memory 406 and persistent storage 408 are computer-readable storagemedia. In this embodiment, memory 406 includes random access memory(RAM) 414 and cache memory 416. In general, memory 406 can include anysuitable volatile or non-volatile computer-readable storage media.

In various embodiments, OSN program 104 is stored in persistent storage408 for execution and/or access by one or more of the respectivecomputer processors 404 via one or more memories of memory 406.Advertising program 106 is stored in persistent storage 408 forexecution and/or access by one or more of the respective computerprocessors 404 via one or more memories of memory 406. In thisembodiment, persistent storage 408 includes a magnetic hard disk drive.Alternatively, or in addition to a magnetic hard disk drive, persistentstorage 408 can include a solid state hard drive, a semiconductorstorage device, read-only memory (ROM), erasable programmable read-onlymemory (EPROM), flash memory, or any other computer-readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 408 may also be removable. Forexample, a removable hard drive may be used for persistent storage 408.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage408.

Communications unit 410, in these examples, provides for communicationswith other data processing systems or devices, including resources ofnetwork 120. In these examples, communications unit 410 includes one ormore network interface cards.

Communications unit 410 may provide communications through the use ofeither or both physical and wireless communications links. Each of OSNsystem 104 and advertising program 106 may be downloaded to persistentstorage 408 through communications unit 410.

I/O interface(s) 412 allows for input and output of data with otherdevices that may be connected to computing device 102. For example, I/Ointerface 412 may provide a connection to external devices 418 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 418 can also include portable computer-readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention (e.g., advertising program 106 canbe stored on such portable computer-readable storage media and can beloaded onto persistent storage 408 via I/O interface(s) 412. I/Ointerface(s) 412 also connect to a display 420.

Display 420 provides a mechanism to display data to a user and may be,for example, a computer monitor, or a television screen.

The term(s) “Smalltalk” and the like may be subject to trademark rightsin various jurisdictions throughout the world and are used here only inreference to the products or services properly denominated by the marksto the extent that such trademark rights may exist.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method for altering aviewing capacity of content displayed via an online social network, themethod comprising: generating, by one or more computer processors, afirst user interface element in conjunction with an advertisementdisplayed on a first online social network user profile in a group ofonline social network user profiles, wherein the first user interfaceelement is a comment box; displaying, by one or more computerprocessors, the advertisement on a second online social network userprofile in the group of online social network user profiles in responseto receiving a comment via the first user interface element; generating,by one or more computer processors, a second user interface element inconjunction with the advertisement displayed on the second online socialnetwork user profile, wherein the second user interface element is acomment box; and displaying, by one or more computer processors,comments received via the first user interface element and the seconduser interface element via the online social network, wherein a viewingcapacity of the comments received via the first user interface elementand the second user interface element is restricted to users associatedwith the group of online social network user profiles.
 2. Thecomputer-implemented method of claim 1, further comprising generatingthe group of online social network user profiles based, at least inpart, on: determining, by the one or more processors, a category of auser of the online social network selected from the group consisting ofa promoter, detractor, and prospect of a product associated with theadvertisement.
 3. The computer-implemented method of claim 2, whereingenerating the group of online social network user profiles is based, atleast in part, on: determining a likelihood of the first user having thefirst online social network user profile to recommend a purchase of aproduct associated with the advertisement to the second user having thesecond online social network user profile.
 4. The computer-implementedmethod of claim 2, wherein generating the group of online social networkuser profiles is based, at least in part, on: determining a likelihoodof the first user having the first online social network user profile topromote an initiation of a conversation about a product associated withthe advertisement to the second user having the second online socialnetwork user profile via the online social network.
 5. Thecomputer-implemented method of claim 2, wherein generating the group ofonline social network user profiles is based, at least in part, on:determining a relationship between the first user having the firstonline social network user profile and the second user having the secondonline social network user profile, wherein the relationship isdetermined based, at least in part, on a frequency of interactionsbetween the first user having the first online social network userprofile and the second user having the second online social network userprofile.
 6. The computer-implemented method of claim 2, whereingenerating the group of online social network user profiles is based, atleast in part, on: determining a likelihood of the first user having thefirst online social network user profile to provide a positive opinionof a product associated with the advertisement.
 7. Thecomputer-implemented method of claim 1, wherein generating the group ofonline social network user profiles is based, at least in part, on:determining a degree of influence of the first user having the firstonline social network user profile based, at least in part, on apurchase of a product by a user in the online social network in responseto the user viewing a comment about the product displayed on the firstonline social network user profile.
 8. A computer program product foraltering a viewing capacity of content displayed via an online socialnetwork, the computer program product comprising: a computer readablestorage medium and program instructions stored on the computer readablestorage medium, the program instructions comprising instructions to:generate a first user interface element in conjunction with anadvertisement displayed on a first online social network user profile ina group of online social network user profiles, wherein the first userinterface element is a comment box; display the advertisement on asecond online social network user profile in the group of online socialnetwork user profiles in response to receiving a comment via the firstuser interface element; generate a second user interface element inconjunction with the advertisement displayed on the second online socialnetwork user profile, wherein the second user interface element is acomment box; and display comments received via the first user interfaceelement and the second user interface element via the online socialnetwork, wherein a viewing capacity of the comments received via thefirst user interface element and the second user interface element isrestricted to users associated with the group of online social networkuser profiles.
 9. The computer program product of claim 8, furthercomprising instructions to generate the group of online social networkuser profiles based, at least in part, on instructions to: determine acategory of a user of the online social network selected from the groupconsisting of a promoter, detractor, and prospect of a productassociated with the advertisement.
 10. The computer program product ofclaim 9, wherein the instructions to generate the group of online socialnetwork user profiles is based, at least in part, on instructions to:determine a likelihood of the first user having the first online socialnetwork user profile to recommend a purchase of a product associatedwith the advertisement to the second user having the second onlinesocial network user profile.
 11. The computer program product of claim9, wherein the instructions to generate the group of online socialnetwork user profiles is based, at least in part, on instructions to:determine a likelihood of the first user having the first online socialnetwork user profile to promote an initiation of a conversation about aproduct associated with the advertisement to the second user having thesecond online social network user profile via the online social network.12. The computer program product of claim 9, wherein the instructions togenerate the group of online social network user profiles is based, atleast in part, on instructions to: determine a relationship between thefirst user having the first online social network user profile and thesecond user having the second online social network user profile,wherein the relationship is determined based, at least in part, on afrequency of interactions between the first user having the first onlinesocial network user profile and the second user having the second onlinesocial network user profile.
 13. The computer program product of claim9, wherein the instructions to generate the group of online socialnetwork user profiles is based, at least in part, on instructions to:determine a likelihood of the first user having the first online socialnetwork user profile to provide a positive opinion of a productassociated with the advertisement.
 14. The computer program product ofclaim 9, wherein the instructions to generate the group of online socialnetwork user profiles is based, at least in part, on instructions to:determine a degree of influence of the first user having the firstonline social network user profile based, at least in part, on apurchase of a product by a user in the online social network in responseto the user viewing a comment about the product displayed on the firstonline social network user profile.
 15. A computer system for altering aviewing capacity of content displayed via an online social network, thecomputer system comprising: one or more computer processors, one or morecomputer readable storage media, and program instructions stored on theone or more computer readable storage media for execution by at leastone of the one or more processors, the program instructions comprisinginstructions to: generate a first user interface element in conjunctionwith an advertisement displayed on a first online social network userprofile in a group of online social network user profiles, wherein thefirst user interface element is a comment box via; display theadvertisement on a second online social network user profile in thegroup of online social network user profiles in response to receiving acomment via the first user interface element; generate a second userinterface element in conjunction with the advertisement displayed on thesecond online social network user profile, wherein the second userinterface element is a comment box; and display comments received viathe first user interface element and the second user interface elementvia the online social network, wherein a viewing capacity of thecomments received via the first user interface element and the seconduser interface element is restricted to users associated with the groupof online social network user profiles.
 16. The computer system of claim15, further comprising instructions to generate the group of onlinesocial network user profiles based, at least in part, on instructionsto: determine a category of a user of the online social network selectedfrom the group consisting of a promoter, detractor, and prospect of aproduct associated with the advertisement.
 17. The computer system ofclaim 16, wherein the instructions to generate the group of onlinesocial network user profiles is based, at least in part, on instructionsto: determine a likelihood of the first user having the first onlinesocial network user profile to recommend a purchase of a productassociated with the advertisement to the second user having the secondonline social network user profile.
 18. The computer system of claim 16,wherein the instructions to generate the group of online social networkuser profiles is based, at least in part, on instructions to: determinea likelihood of the first user having the first online social networkuser profile to promote an initiation of a conversation about a productassociated with the advertisement to the second user having the secondonline social network user profile via the online social network. 19.The computer system of claim 16, wherein the instructions to generatethe group of online social network user profiles is based, at least inpart, on instructions to: determine a relationship between the firstuser having the first online social network user profile and the seconduser having the second online social network user profile, wherein therelationship is determined based, at least in part, on a frequency ofinteractions between the first user having the first online socialnetwork user profile and the second user having the second online socialnetwork user profile.
 20. The computer system of claim 16, wherein theinstructions to generate the group of online social network userprofiles is based, at least in part, on instructions to: determine alikelihood of the first user having the first online social network userprofile to provide a positive opinion of a product associated with theadvertisement.